Sustainable Development, Urban Growth/Sprawl, and Infrastructure System

In the recent years ‘sustainable development’ is a commonly used terminology among various sections of the society. Sustainable development is defined as, “development that meets the needs of the present without compromising the ability of the future generations to meet their own needs” (WCED 1987). Sustainable development is a pattern of resource-use that aims to meet human needs while preserving the environment so that these needs can be met not only in the present, but also for future generations. In order to sustain a development, the supply and quality of major consumables and inputs to our daily lives and economic production--such as air, water, energy, food, raw materials, land, and the natural environment need to be taken care of.

Sustainable development does not focus solely on environmental issues. The United Nations 2005 World Summit Outcome Document refers to the “interdependent and mutually reinforcing pillars” of sustainable development as economic development, social development, and environmental protection (United Nations 2005). According to Hasna (2007), sustainability is a process which tells of a development of all aspects of human life affecting sustenance. It means resolving the conflict between the various competing goals, and involves the simultaneous pursuit of economic prosperity, environmental qualityand social equity; hence it is a continually evolving process. The ‘journey’ (the process of achieving sustainability) is of course vitally important, but only as a means of getting to the destination (the desired future state). However, the ‘destination’ of sustainability is not a fixed place in the normal sense that we understand destination. Instead, it is a set of wishful characteristics of a future system.


2. Urban Growth and Sprawl

Urban growth is a spatial and demographic process and refers to the increased importance of towns and cities as a concentration of population within a particular economy and society. It occurs when the population distribution changes from being largely hamlet and village based to being predominantly town and city dwelling (Clark 1982). The spatial configuration and the dynamics of urban growth are important topics of analysis in the contemporary urban studies. Several studies have addressed these issues with or without the consideration of demographic process and urbanisation which have dealt with diverse range of themes (e.g., Acioly and Davidson 1996; Wang et al. 2003; Páez and Scott 2004; Zhu et al. 2006; Belkina 2007; Puliafito 2007; Yanos 2007; Martinuzzi et al. 2007; Hedblom and Soderstrom 2008; Zhang and Atkinson 2008; Geymen and Baz 2008).

The first and foremost reason of urban growth is increase in urban population. The rapid growth of urban areas is the result of two population growth factors: (1) natural increase in population, and (2) migration to urban areas. Natural population growth results from excess of births over deaths. Migration is defined as the long-term relocation of an individual, household or group to a new location outside the community of origin. In the recent time, the movement of people from rural to urban areas within the country (internal migration) is most significant. According to the United Nations report (UNFPA 2007), the number and proportion of urban dwellers will continue to rise quickly. Urban global population will grow to 4.9 billion by 2030. In comparison, the world’s rural population is expected to decrease by some 28 million between 2005 and 2030. At the global level, all future population growth will thus be in towns and cities; most of which will be in developing countries. The urban population of Africa and Asia is expected to be doubled between 2000 and 2030.

This huge growth in urban population will force in uncontrolled urban growth resulting in sprawl. Urban sprawl is the less compact outgrowth of a core urban area exceeding the population growth rate and having a refusal character or impact on sustainability of environment and human. The rapid growth of cities strains their capacity to provide services such as energy, education, health care, transportation, sanitation and physical security. Because governments have less revenue to spend on the basic upkeep of cities and the provision of services, cities will become areas of massive sprawl and serious environmental problems threatening the regional sustainability.


3. Infrastructure System

Infrastructure can be defined as the basic physical and organizational structures needed for the operation of a society or enterprise, or the services and facilities necessary for an economy to function, or a set of assets needed to supply certain desired services. Infrastructure has several layers of implementation, such as, physical infrastructure (e.g. transport, energy, water and telecommunications infrastructure), social infrastructure (e.g. the education and health systems), environmental infrastructure (e.g. national parks system), institutional infrastructure (e.g. the land use planning system), neighbourhood infrastructure (e.g., parcel system, lanes/by-lanes), community-level infrastructure (e.g., water, sewer, power), amongst others. All of these interact with other in complex relations and often these systems are overlapping. In the recent days, studies are essentially needed that address the nature and magnitude of the positive and negative links between infrastructure systems and the other various components of sustainable development: social, economic, and environmental.


4. Urban Sprawl, Infrastructure System and Sustainability

Urban sprawl has widely been discussed due to its ill effects on environment (Kirtland et al. 1994). However, sprawl is also blamed as being inordinately costly to its occupants and to society (Harvey and Clark 1965). It is blamed due to its economic cost (Buiton 1994). Cities have experienced an increase in demand for public services and for the maintenance and improvement of urban infrastructures (Barnes et al. 2001) such as fire-service stations, police stations, schools, hospitals, roads, water mains, and sewers in the countryside. Sprawl requires more infrastructures, since it takes more roads, pipes, cables and wires to service these low-density areas compared to more compact developments with the same number of households. 

The Costs of Sprawl and other studies have shown that development of neighbourhood infrastructure becomes less costly on a per-unit basis as density rises (for a review of literature, see Priest et al. 1977; Frank 1989; Bhatta 2010). As long as developers are responsible for the full costs of neighbourhood infrastructure, and pass such costs on to homebuyers and other end-users of land, lower-density development patterns will meet the test of economic efficiency (at least with respect to infrastructure costs). Where inefficiency is more likely to arise is in the provision of community-level infrastructure. Inefficiency may also arise in the operation and maintenance of infrastructure, and in the provision of public services. Because people are more dispersed and no longer residing in centralized cities, the costs of community infrastructure and public services in suburban areas increases (Brueckner 2000; Heimlich and Anderson 2001; Pedersen et al. 1999; Wasserman 2000). These costs tend to be financed with local taxes or user fees that are generally independent of location, causing remote development to be subsidized. From the standpoint of community-level infrastructure, costs do not vary so much with residential density but with the degree of clustering and/or proximity to existing development (Stone 1973; RERC 1974; Downing and Gustely 1977; Peiser 1984).

The preceding discussion directs our attention towards the challenges for sustainable development of infrastructure systems. This should ground the basis of immediate initiatives from all of the layers of the society—politicians, planners & administrators, enforcements, NGOs, environmentalists, stakeholders, and the general citizens as well. Achieving the goals of sustainability is indeed real challenging.


References

Acioly, C.C. and Davidson, F. (1996). Density in Urban Development. Building Issues, 8(3): 3–25.
Banerjee, A. (2005). Population growth, environment and development: some issues in sustainability of the mega city of Kolkata (Calcutta), West Bengal. Proceedings of the National Seminar on Population Environment and Nexus, 21 October, Deonar, Mumbai: Population Environment Centre, IIPS. Available at:http://www.iipsenvis.nic.in/paper/fp_anuradhab.pdf Accessed 13.02.08.
Barnes, K.B., Morgan III, J.M., Roberge M.C. and Lowe, S. (2001). Sprawl development: Its patterns, consequences, and measurement. Towson University: A white paper. Available at:http://chesapeake.towson.edu/landscape/ ... _paper.pdf
Belkina, T.D. (2007). Diagnosing Urban Development by an Indicator System. Studies on Russian Economic Development, 18(2): 162–170.
Bhatta, B. (2010). Analysis of Urban Growth and Sprawl from Remote Sensing Data. Springer-Verlag, Heidelberg, pp. 170.
Brueckner, J.K. (2000). Urban sprawl: Diagnosis and remedies. International Regional Science Review, 23(2): 160–171.
Buiton, P.J. (1994). A Vision for Equitable Land Use Allocation, Land Use Policy, 12(1): 63–68.
Clark, D. (1982). Urban Geography: An Introductory Guide. Taylor & Francis.
Downing, P.B. and Gustely R.D. (1977). The Public Service Costs of Alternative Development Patterns: A Review of the Evidence. In P.B. Downing (ed.), Local Service Pricing Policies and Their Effect on Urban Spatial Structure, Vancouver, B.C: University of British Columbia Press.
Frank, J.E. (1989). The Costs of Alternative Development Patterns: A Review of the Literature. Washington D.C.: Urban Land Institute.
Geymen, A. and Baz, I. (2008). Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area. Environmental Monitoring Assessment, 136: 449–459.
Harvey, R.O., and Clark, W.A.V. (1965). The nature and economics of urban sprawl. Land Economics, 41(1): 1–9.
Hasna, A.M. (2007). Dimensions of sustainability. Journal of Engineering for Sustainable Development: Energy, Environment, and Health, 2(1): 47–57.
Hedblom, M. and Soderstrom, B. (2008). Woodlands across Swedish urban gradients: Status, structure and management implications. Landscape and Urban Planning, 84: 62–73.
Heimlich, R.E. and Anderson, W.D. (2001, June). Development at the urban fringe and beyond: Impacts on agriculture and rural land. ERS Agricultural Economic Report No. 803, pp. 88.
Kirtland, D., Gaydos, L., Clarke, K., DeCola, L., Acevedo, W. and Bell, C. (1994). An analysis of human-induced land transformations in the San Francisco Bay/Sacramento area. World Resources Review, 6(2): 206–217.
Martinuzzi, S., Gould, W.A. and Gonzalez, O.M.R. (2007). Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data. Landscape and Urban Planning, 79: 288–297.
Páez, A. and Scott, D.M. (2004). Spatial statistics for urban analysis: A review of techniques with examples. GeoJournal, 61: 53–67.
Pedersen, D., Smith, V. E. and Adler, J. (1999, July 19). Sprawling, sprawling ……. Newsweek, 23– 27.
Peiser, R.B. (1984). Does It Pay to Plan Suburban Growth? Journal of the American Planning Association, 50(4): 419–433.
Priest, D. et al. (1977). Large-Scale Development: Benefits, Constraints, and State and Local Policy Incentives. Washington D.C.: Urban Land Institute, pp. 37–45.
Puliafito, J.L. (2007). A transport model for the evolution of urban systems. Applied Mathematical Modelling, 31: 2391–2411.
RERC (Real Estate Research Corporation) (1974). The Costs of Sprawl, Detailed Cost Analysis. Washington, D.C.: U.S. Government Printing Office.
Stone, P.A. (1973). The Structure, Size, and Costs of Urban Settlements. London: Cambridge University Press.
UNFPA (United Nations Population Fund) (2007). Peering into the dawn of an urban millennium, State of world population 2007: Unleashing the potential of urban growth. Available at:www.unfpa.org/swp/2007/english/introduction.html
United Nations (2005). World Summit Outcome Document, World Health Organization.
Wang, W., Zhu, L., Wang, R. and Shi, Y. (2003). Analysis on the spatial distribution variation characteristic of urban heat environmental quality and its mechanism – A case study of Hangzhou City. Chinese Geographical Science, 13(1): 39–47.
Wasserman, M. (2000). Confronting urban sprawl. Regional Review of the Federal Reserve Bank of Boston, 9–16.
WCED (World Commission on Environment and Development) (1987). Our Common Future. Oxford: Oxford University Press.
Yanos, P.T. (2007). Beyond “Landscapes of Despair”: The need for new research on the urban environment, sprawl, and the community integration of persons with severe mental illness. Health & Place, 13: 672–676.
Zhang, P. and Atkinson, P.M. (2008). Modelling the effect of urbanization on the transmission of an infectious disease. Mathematical Biosciences, 211: 166–185.
Zhu, M., Xu, J., Jiang, N., Li, J. and Fan, Y. (2006). Impacts of road corridors on urban landscape pattern: a gradient analysis with changing grain size in Shanghai, China. Landscape Ecology, 21: 723–734.

Global Navigation Satellite System (GNSS) and its Definitions

Often my students ask about a clear definition of Global Navigation Satellite System (GNSS); since in many instances the definitions of GNSS are application specific and not lucid.

As it is known to us that GNSS is a satellite based navigation and positioning system. This system provides autonomous spatial positioning with global coverage. A GNSS allows small electronic receiver to determine its location using signals transmitted from navigation satellites. For anyone with a GNSS receiver, the system can provide location (and time) information in all weather conditions, day and night, anywhere in the world.

GNSS is made up of three segments: (1) satellites orbiting the earth; (2) control and monitoring stations on the earth; and (3) the GNSS receivers owned by users. GNSS satellites broadcast signals from space that are picked up and identified by GNSS receivers. Each GNSS receiver then provides three-dimensional location (latitude, longitude, and altitude), precise time information, and other information for calibration purposes.

Individuals may purchase GNSS receivers that are readily available through commercial retailers. Equipped with these receivers, users can accurately locate where they are and can easily navigate to where they want to go, whether walking, driving, flying, or sailing. GNSS has become a mainstay of transportation systems worldwide, providing navigation for aviation, ground, and maritime operations. Disaster relief and emergency services depend upon GNSS for location and timing capabilities in their life-saving missions. Activities such as banking, mobile phone operations, and even the control of power grids, are facilitated everyday by the accurate timing provided by GNSS. Engineers, surveyors, geologists, geographers, and countless others can perform their work more efficiently, safely, economically, and accurately using the GNSS technology.

There are currently two GNSSs in operation: the United States’ NAVigation Satellite Timing And Ranging Global Positioning System (NAVSTAR GPS, commonly known as GPS) and the Russian GLObal'naya NAvigatsionnaya Sputnikovaya Sistema (GLONASS). A third system, Galileo, is currently being developed in Europe; and a fourth, Compass Navigation Satellite System (or Beidou II; commonly referred as Compass) has been initiated by China. Other than these global systems there are some regional or local systems as well.

With the advent of GPS and GLONASS, and soon with the addition of Galileo and Compass, the application by civil users of global positioning, navigation, and timing services has mushroomed around the world and has popularized the concept of GNSS. Unfortunately we have yet to come up with a commonly accepted and actionable definition of GNSS (Swider 2005). Swider (2005) has defined GNSS as:
GNSS collectively refers to the worldwide civil positioning, navigation, and timing determination capabilities available from one or more satellite constellations. 

A definition of GNSS given by International Civil Aviation Organization is (ICAO 2005):
GNSS is a world-wide position and time determination system that includes one or more satellite constellations, aircraft receivers, system integrity monitoring augmented as necessary to support the required navigation performance for the intended operation. 

Another simple definition is:
GNSS is a satellite-based system that is used to pinpoint the geographic location of a user’s receiver anywhere in the world. 

The above definition is short, simple, and memorable; however, it is technologically not sound enough. A better definition of GNSS is (Bhatta 2008):
GNSS is a network of satellites that continuously transmits coded information, which makes it possible to precisely identify locations on the earth by measuring distances from the satellites. 

Whatever the earlier definitions we may find in the literature, a good definition of GNSS can be given as (Bhatta 2010):
GNSS is a system consisting network of navigation satellites monitored and controlled by ground stations on the earth, which continuously transmit radio signals that are captured by the receivers to process, and thus to make it possible to precisely geolocation of the receiver by measuring distances from the satellites and to provide precise time information any were in the world at any time.
'Geolocation' refers to identifying the real-world geographic location of a GNSS receiver.


References
Swider, R.J. 2005, Can GNSS Become a Reality?, GPS World, 16(12): 20–20.
ICAO 2005, Draft Galileo SARPS – Part A, Working Paper, International Civil Aviation Organization NSP/WG1: WP35, 12 pp.
Bhatta, B. 2008, Remote Sensing and GIS, Oxford University Press, New York, 872 pp.
Bhatta, B. 2010, Global Navigation Satellite Systems : Insights into GPS, GLONASS, Galileo, Compass, and Others, BS Publications, Hyderabad, 438 pp.

Geostatistics

Geostatistics is an application of the theory of random functions for estimating natural phenomena. ‘Geostatistics offers a way of describing the spatial continuity of natural phenomena and provides adaptations of classical regression techniques to take advantage of this continuity’ (Isaaks and Srivastava 1989). The data that we have are never complete; we have either the wrong kind or insufficient or partial coverage. Naturally, we seek ways to predict the values between, or to extrapolate beyond, the limits of our data.

The basic concept of geostatistics is that of scales of spatial variation. Data which are spatially independent show the same variability regardless of the location of data points. However, spatial data in most cases are not spatially independent. Data values which are close spatially show less variability than data values which are farther away from each other. A fundamental concept in geography is that nearby entities often share more similarities than entities which are far apart (Miller 2004). This idea is often labelled ‘Tobler’s first law of geography’ and may be summarised as ‘everything is related to everything else, but near things are more related than distant things’ (Tobler 1970). The exact nature of this pattern varies from data set to data set; each set of data has its own unique function of variability and distance between data points. This variability is generally computed as a function called semivariance.

In one respect geostatistics might be viewed as simply a methodology for interpolating data on an irregular pattern but this is too simplistic. A number of interpolation methods/algorithms were already well known when geostatistics began to be known; for example, inverse distance weighting (IDW) and trend surface analysis as well as the much simpler nearest neighbor algorithm. Interpolation techniques use sample points to produce surfaces of the phenomena of interest. The interpolation techniques are divided into two main types: deterministic and geostatistical methods.

Deterministic interpolation techniques create surfaces from measured points, based on either the extent of similarity (e.g., IDW) or the degree of smoothing (e.g., radial basis functions). These techniques do not use a model of random spatial processes. A deterministic interpolation can either force the resulting surface to pass through the data values or not. An interpolation technique that predicts a value that is identical to the measured value at a sampled location is known as an exact interpolator (e.g., IDW and radial basis functions). An inexact interpolator (e.g., global and local polynomials) predicts a value that is different from the measured value. The latter can be used to avoid sharp peaks or troughs in the output surface.

Geostatistics assume that at least some of the spatial variation of natural phenomena can be modeled by random processes with spatial autocorrelation. Geostatistical techniques produce not only prediction surfaces but also error or uncertainty surfaces, giving us an indication of how good the predictions are. Geostatistical interpolators exhibit probabilistic behaviour, i.e., it can be considered that for one known condition there are many possible outcomes, some of which will be more likely than others.

Many methods are associated with geostatistics, but they generally fall in the kriging family, e.g., ordinary, simple, universal, probability, indicator, and disjunctive kriging, along with their counterparts in cokriging. Kriging is a method of estimation based on the trend and variability from the trend. Variability, in this context, refers to random errors about the trend or mean. In this context, ‘error’ does not imply a mistake but a fluctuation (error) about the trend is unknown and is not systematic; the fluctuation could be positive or negative. Kriging may be considered exact (or smoothed) or inexact. Kriging incorporates the principles of probability and prediction, and like the IDW, is a weighted average technique except that a surface produced by kriging may exceed the value range of the sample points while still not actually passing through them. Various statistical models can be chosen to produce map outputs (or surfaces) from the kriging process; such as, interpolated surface (the prediction), the standard prediction errors (variance), probability (that the prediction exceeds a threshold) and quantile (for any given probability) (Liu and Mason 2009). One may refer Isaaks and Srivastava (1989) for an introductory text on Geostatistics.


References
Isaaks, E.H. and R.M. Srivastava 1989, An Introduction to Applied Geostatistics, Oxford University Press, New York, 561 pp.
Miller, H.J. 2004, ‘Tobler’s first law and spatial analysis’, Annals of the Association of American Geographers 94: 284–289.
Tobler, W. 1970, ‘A computer movie simulating urban growth in the Detroit region’, Economic Geography 46: 234–240.
Liu, J.G. and P.J. Mason 2009, Essential Image Processing and GIS for Remote Sensing, Wiley-Blackwell, New York, 450 pp.

Classification of Remote Sensing

[Excerpted from my book Remote Sensing and GIS]

Remote sensing is a complex technique and may vary based on the application and technological development. Considering technological development, for example, in the earlier days remote sensing was performed from balloons, but nowadays satellites are being used. Earlier, photographic cameras remained the only option, but nowadays digital cameras/sensors are dominating. Considering applications, for example, mapping purposes can be fulfilled by optical images, but information about temperature needs thermal image.

Remote sensing may be classified from many perspectives, for example, based on platform, source of energy, number of bands, and so on. The following sections explain remote sensing from the perspective of different classification schemes.


Classification Based on Platform

In order for a remote sensor to collect and record energy reflected or emitted from a target or surface, it must reside on a stable platform away from the target or surface being observed. Platforms for remote sensors may be situated on the ground, on an aircraft, or balloon (or some other platform within the earth’s atmosphere), or on a spacecraft or satellite outside the earth’s atmosphere.

Ground-based sensors are often used to record detailed information about the surface that is compared with information collected from aircraft or satellite sensors. In some cases, this can be used to better characterize the target that is being imaged by these other sensors, making it possible to better understand the information in the imagery. Sensors may be placed on a ladder, scaffolding, tall building, cherry-picker, crane, etc.

However, remotely sensed data are mainly collected either from the platforms within the earth’s atmosphere (air), or platforms in the space (outside of earth’s atmosphere). Platforms within the air are called aerial or airborne platforms, and platforms in the space are called space-borne or space platforms. Accordingly remote sensing is also referred as aerial or airborne or sub-orbital remote sensing, and space or space-borne or orbital remote sensing.

Different aerial platforms are balloons, kites, pigeons, aircrafts, etc. Balloons, kites, and pigeons are the early platforms of remote sensing and currently not used. Aircrafts are the main aerial platforms. An aircraft is a vehicle which is able to fly by being supported by the air. In remote sensing, aircrafts are primarily stable wing aeroplanes, although helicopters are also occasionally used.

In space, remote sensing is conducted mainly from satellites, and it is called satellite remote sensing. It is also known as satellite-borne remote sensing. Satellites are objects which revolve around another object--in this case, the earth. For instance, the moon is a natural satellite, whereas man-made satellites include platforms that are launched for remote sensing, communication, and telemetry (location and navigation) purposes.

Remote sensing, in space, may also be performed from space stations (such as, International Space Station); however it is a rare case. Other rarely used sensor platform is space transport system, commonly known as space shuttle. Data acquired from space station and space shuttle are used for scientific experimentations; they are not available commercially or widely.

Cost is often a significant factor in choosing among the various platform options. Moreover, each of the platforms has its own advantages and disadvantages. Satellite remote sensing can significantly enhance the information available from traditional data sources because it can provide synoptic view of large portions of the earth. Satellite imagery can also expand the spatial dimensions of limited and sometimes costly field or point-source sampling efforts. Some satellite sensors cover areas that may be physically or politically inaccessible, or that are too vast to survey with traditional methods. Satellite remote sensing can also provide consistent repeat coverage at relatively frequent intervals, making detection and monitoring of change feasible. Satellite-derived data and information are also useful for applications that require fine spatial resolution such as surveys of urban and suburban land-use/land-cover, for agricultural purposes, and natural resources; surveys for coastal management; and measurements of water quality in limnological (concerning lake and other fresh waters) and oceanographic applications.

The disadvantages of satellite remote sensing include the inability of many sensors to obtain data and information through cloud cover (although microwave sensors can image the earth through clouds) and the relatively low spatial resolution achievable with many satellite-borne earth remote sensing instruments.

In addition, the need to correct for atmospheric absorption and scattering, and for the absorption of radiation through water on the ground can make it difficult to obtain desired data and information on particular variables. Satellite remote sensing creates large quantities of data that typically require extensive processing as well as storage and analysis.

Finally, data from satellite remote sensing are often costly if purchased from private vendors or value-adding resellers, and this initial cost, together with intellectual property restrictions, can limit the dissemination of products from such sources.

In many instances, there may be an advantage of combining the large-scale, synoptic data that are accessible from space with higher-resolution surveys of key locations that can be made from other platforms, such as aircraft. Aerial photography, for instance, has a competitive advantage in applications that require fine spatial resolution of small areas or that involve areas subject to frequent cloud cover, especially in cases where repeat coverage is not needed (mobilizing the aircraft repeatedly will be a costly process). Another advantage of aerial photography is that surveys can be scheduled for specific purposes, time, and locations. But aircraft cannot be mobilized in politically inaccessible areas.


Classification Based on Energy Source

As we know the sun is the natural source of energy or radiation. The sun provides a very suitable source of energy for remote sensing. This energy is either reflected, as it is for visible and reflective IR wavelengths, or absorbed and then reemitted, as it is for thermal infrared wavelengths. Remote sensing systems which measure energy that is naturally available are called passive remote sensing. Passive sensors can only be used to detect naturally occurring energy. Passive remote sensing can only take place during the time when the sun is illuminating the earth, because the sun is the natural source of energy. There is no reflected energy available from the sun at night. Energy which is naturally emitted (such as thermal infrared) can be detected day or night, as long as the amount of energy is large enough to be recorded.

Active sensors, on the other hand, provide their own energy source for illumination. The sensor emits radiation, which is directed towards the target to be investigated. The radiation reflected from that target is then detected and measured by the sensor. Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of the day or season. Active sensors can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. However, active systems require the generation of a fairly large amount of energy to adequately illuminate targets. A laser fluorosensor and synthetic aperture radar (SAR) are some examples of active sensors.


Classification Based on Imaging Media 

Reflected or emitted energy from terrain may be imaged, either photographically or electronically (digitally). The photographic imaging process uses chemical reactions on the surface of light-sensitive film to detect and record energy variations. In the case of digital imaging, sensors use electronic transducers such as charge coupled devices (CCDs).

Since its inception, photographic imaging system for remote sensing has been widely used from aerial platforms. Other platforms like space shuttle and early experimental spacecrafts had also been used for photographic imaging on experimental basis. However, in applied remote sensing, these platforms do not have any significance and aeroplane is the only platform used for photographic imaging. Digital imaging is rather a new technique that is being used from satellites as well as aeroplanes. Important to realize that satellite remote sensing is based on digital imaging; because a satellite remains on its orbit throughout its life and there is no chance of getting the film if recorded photographically. Digitally recorded data are transmitted from the satellite to the earth via digital communication link.

Photographic remote sensing is possible only within the range of photographic region (i.e. 0.3–0.9 micrometer) of electromagnetic spectrum. Therefore, digital imaging is the only choice if the sensor uses wavelengths that are outside of this region. Digital technique is capable of much higher spectral resolution than photographic systems. Multi-band or multispectral photographic systems use separate lens systems to acquire each spectral band. This may lead to problems in ensuring that the different bands are comparable both spatially and radiometrically and with registration of the multiple images. Digital systems acquire all spectral bands simultaneously through the same optical system to alleviate these problems. Photographic systems record the energy detected by means of a photochemical process, which is difficult to measure, and to maintain consistency. Because digital image data are recorded electronically, it is easier to determine the specific amount of energy measured, and they can record over a greater range of values. Photographic systems require a continuous supply of film and processing on the ground after the photos have been taken. The digital recording systems facilitate transmission of data to receiving stations on the ground and immediate processing of data in a computer environment.


Classification Based on the Regions of Electromagnetic Spectrum

As discussed in Chapter 1, remote sensing may be performed in different regions of electromagnetic spectrum. Remote sensing can also be classified based on the regions of electromagnetic spectrum in use. Optical remote sensing is performed within the optical region (0.3–3.0 micrometer), photographic remote sensing is performed within the photographic region (0.3–0.9 micrometer), thermal remote sensing uses the thermal region (3.0 micrometer – 1 mm), and microwave remote sensing is conducted within the microwave region (1 mm – 1 m). Optical and photographic remote sensing records reflected energy from the earth’s surface. These sensors generally use the sun as a source of energy (an exception is LiDAR). Thermal and passive microwave remote sensing uses emitted energy from the earth’s surface. However, active microwave remote sensing throws artificially generated energy to the earth’s surface and then the backscattered energy is recorded by the sensor. Backscatter is the term given to reflections in the opposite direction to the incident active microwave rays.

Several other techniques, for example LiDAR and SONAR, are also available. However, these are not widely used and difficult to understand at this point of discussion. The use of different wavelengths (and thereby techniques) is mainly because of different applications.


Classification Based on Number of Bands

Images for a geographic area may be collected in single band or more than one bands. Remote sensing can also be classified based on the number of bands to which a sensor is sensitive.

Panchromatic remote sensing is defined as the collection of reflected, emitted, or backscattered energy from an object or area of interest in a single band of the electromagnetic spectrum. In this case, generally, images are collected within the visible region (i.e., 0.4–0.7 micrometer); however, in some of the instances a wider region is also used (e.g., 0.3–0.9 micrometer). Therefore, if a sensor captures images in single band in microwave region it can not be said as panchromatic image. It must use the visible region or a wider region that essentially contains visible region.

Multi-spectral remote sensing is defined as the collection of reflected, emitted, or backscattered energy from an object or area of interest in multiple bands of the electromagnetic spectrum. In order to increase the spectral discrimination, remote sensing systems designed to monitor the earth’s surface, employ a multi-spectral design. Multi-spectral sensors can detect energy in a less number of broad wavelength bands. Multi-spectral remote sensing may be performed in the optical, thermal, as well as microwave regions. However, sensors and imaging techniques are different for different regions.

Hyper-spectral remote sensing is a major advancement in remote sensing, which is currently coming into its own as a powerful and versatile means for continuous sampling of narrow intervals of the spectrum. It is, in many respects, just an extension of the techniques employed in multi-spectral remote sensing. Multi-spectral remote sensors produce images with a few relatively broad wavelength bands. Hyper-spectral remote sensors, on the other hand, collect image data simultaneously in dozens or hundreds of narrow (as little as 0.01 micrometer in width for each), adjacent spectral bands. Hyper-spectral remote sensing is generally performed within the optical and thermal region of electromagnetic spectrum.

Panchromatic remote sensing can be conducted either photographically or digitally. Similarly, multi-spectral remote sensing can also be conducted photographically or digitally if it is performed within the photographic region. However, if it includes wavelengths outside of the photographic region then it must use digital imaging. Multi-spectral remote sensing, nowadays, is conducted digitally. For the hyper-spectral remote sensing, digital technique is the only method; because, photographic film can not be used to capture such a narrow spectral band.

Geographic(al) Information System, GIScience, Geomatics, Geoinformatics, Geoinformation Technology and Geospatial Technology

 [Excerpted from my book Remote Sensing and GIS]

Common people, often, get confused with the terms Geographic(al) Information System, GIScience, Geomatics, Geoinformatics, Geoinformation Technology and Geospatial Technology. To understand the differences or similarities among them we need to fine-tune our understanding about these frequently used and interchangeable terms.

Geographic Information System (GIS) is a computer-based information system used to digitally represent and analyze the geospatial data or geographic data. The GIS has been called an 'enabling technology', because it offers interrelation with the wide variety of disciplines which must deal with geospatial data. Each related field provides some of the techniques which make up a GIS. Many of these related fields emphasize data collection; GIS brings them together by emphasizing integration, modelling, and analysis. GIS has many alternative names used over the years with respect to the range of applications and emphasis; e.g., land information system, AM/FM--automated mapping and facilities management, environmental information system, resources information system, planning information system, spatial data-handling system, soil information system, and so on.

However, GIS may be considered as a type of software in a computer system that allows us to handle information about the location of features or phenomena on the earth’s surface, which has all the functionalities of a conventional DBMS plus much of the functionality of a computer mapping system. But software or an information system cannot be used in a vacuum. We need proper knowledge to develop it, to use it, and to make decisions from it. From this point of view, GIS is not just an advanced type of information systems, but a combination of science and technology, which has several interrelated distinct disciplines. Some of the interrelated important disciplines are geography, cartography, remote sensing, photogrammetry, surveying, geodesy, global navigation satellite system (GNSS), statistics, operations research, computer science, mathematics, and civil engineering.

As the integrating field, GIS often claims to be a science--Geospatial Information Science or Geographic Information Science. In the strictest sense, GIS is a computer system capable of integrating, storing, editing, analyzing, sharing, and displaying geographically referenced information. In a more generic sense, GIS is a tool that allows users to create interactive queries (user defined searches), analyze the geospatial information, and edit geospatial data. Geographical Information Science (often written as GIScience) is the science underlying the applications and systems. It is closely related to GIS but is not application-specific like GIS. For instance, analysis techniques, visualisation techniques, and algorithms/scientific logics for geographical data analysis are all part of GIScience.

GIScience is very much related with the term Geoinformatics that is a shorter name for Geographic Information Technology. Geographic information (also called geoinformation) is created by manipulating geographic (or geospatial) data in a computer system. Geoinformatics is a science and technology, which develops and uses information science infrastructure to address the problems of Geosciences (another name for Earth sciences) and related branches of engineering. Prakash (2006) defined Geoinformatics as "the collection, integration, management, analysis, and presentation of geospatial data, models and knowledge that support disciplinary, multidisciplinary, interdisciplinary and transdisciplinary research and education". The four main tasks of Geoinformatics are: (1) collection and processing of geodata (geodata is the contraction of geographic data), (2) development and management of databases of geodata, (3) analysis and modelling of geodata, and (4) development and integration of logic and computer tools and software for the first three tasks. Geoinformatics uses GeoComputation (see note below) and it is the development and use of remote sensing, GIS, and GNSS.

According to Virrantaus and Haggrén (2000) geoinformatics is a combination of remote sensing and GIS (they used the term Geoinformation Technique (GIT) instead of GIS technology). For example spatial analysis is a field in which image processing and GIS software tools are mixed and used together. It is very good experience to realize how same functionality can be achieved by using either image processing software tool or traditional GIS analysis tool within the embrace of Geoinformatics.

Geoinformatics is not only for the people from surveying or geography but recently more and more people from other disciplines like Computer Science, Civil Engineering, Architecture, Geology etc. want to study Geoinformatics as their minor or even as their major subject (Virrantaus and Haggrén 2000). For that reason it has been most important to develop the contents of Geoinformatics curriculum towards more scientific subject and less being related with traditional surveying and mapping. People who wish to apply RS and GIS in their own problems among landscape design, geology or software development do not want to get profound knowledge on field measurements or printing technology. Geoinformatics as a mathematically and computationally oriented subject concentrates on data modeling and management, analysis and visualization processes and algorithms, GeoComputation, spatial statistics and operations research applications, development of GIS, image interpretation and satellite mapping technology (Virrantaus and Haggrén 2000).

Geoinformatics is a subset of Geomatics (also called Geomatics Engineering). In addition to topics within the confines of Geoinformatics, Geomatics emphasizes traditional surveying and mapping. The term 'Geomatics' relates both to science and technology, and integrates the following more specific disciplines and technologies: geodesy, traditional surveying, GNSS and their augmentations, cartography, remote sensing, photogrammetry, and GIS. An alternative view is that geomatics is the measurement and survey component of the broader field of GISscience. Geomatics is the discipline of gathering, storing, processing, and delivering of geoinformation or spatially referenced information.

The term Geomatics is fairly young, apparently being coined by B. Dubuisson in 1969. Originally used in Canada, because it is similar in French and English, the term geomatics has been adopted by the International Organization for Standardization, the Royal Institution of Chartered Surveyors, and many other international authorities, although some (especially in the United States) have shown a preference for the term 'Geospatial Technology'.

Geomatics (or Geospatial Technology) is all about geospatial data. Although, precise definition of geomatics is still in flux; a good definition can be given from the University of Calgary's web page: "Geomatics Engineering is a modern discipline, which integrates acquisition, modelling, analysis, and management of spatially referenced data, i.e. data identified according to their locations. Based on the scientific framework of geodesy, it uses terrestrial, marine, airborne, and satellite-based sensors to acquire spatial and other data. It includes the process of transforming spatially referenced data from different sources into common information systems with well-defined accuracy characteristics". Konecny (2002) said "Geomatics, composed of the disciplines of geopositioning, mapping and the management of spatially oriented data by means of computers, has recently evolved as a new discipline from the integration of surveys and mapping (geodetic engineering) curricula, merged with the subjects of remote sensing and GIS". Geopositioning refers to identifying the real-world geographic position by means of GNSS or any other surveying technique.

A number of University Departments which were once titled Surveying, Survey Engineering or Topographic Science, have re-titled themselves as Geomatics or Geomatics Engineering. According to Konecny (2002), geomatics has originated from surveying, mapping, and geodesy. Earlier, in higher education, the specialization was possible in one field such as geodesy or photogrammetry, but a comprehensive orientation toward surveying and mapping was lacking. Since about 1960 a technological revolution has taken place in surveying and mapping technology: angular surveys have been augmented by electronic distance measurement, and more recently by GNSS. Digital computers were able to statistically analyze huge measurement sets. Photogrammetry has become an analytical discipline, competing in accuracy with ground surveys. Earth observation by satellites has made remote sensing an indispensable tool. Cartography relying on tedious graphic work has made way to computer graphics. GIS has permitted to organize spatially oriented data in databases for the management of global, regional and local problems. The need for sustainable development has recently made obvious, that spatially referenced data constitute a needed infrastructure (spatial data infrastructure), to which all governments subscribe. Surveying and mapping curricula have traditionally provided the vision for the provision, updating, management and dissemination of spatially referenced data. However, there was a need to upgrade the curriculum orientation to modern tools and to society's requirements. This is the reason why many programs have changed their name to 'Geomatics'.

NOTE
GeoComputation is an emergent paradigm (class of elements with similarities) for multidisciplinary/interdisciplinary research that enables the exploration of previously insolvable, extraordinarily intricate problems in geographic context. Some people see GeoComputation as an incremental development rather than something entirely new. Several doubt that GeoComputation will make any real contribution to the sciences. Others view GeoComputation as a follow-on revolution to GIS. Openshaw (2000) argues GeoComputation is not just using computational techniques to solve spatial problems, but rather a completely new way of doing science in a geographical context.


References
Konecny, G. (2002). Recent global changes in geomatics education. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXIV, Part 6, pp. 9-14.
Openshaw, S. (2000). GeoComputation. In: S. Openshaw and R.J. Abrahart (eds.), GeoComputation, Taylor & Francis, New York, pp. 1-31.
Prakash, A. (2006). Introducing Geoinformatics for Earth System Science Education. Journal of Geoscience Education. URL: http://findarticles.com/p/articles/mi_q ... _n17190422
University of Calgary's web page: http://www.geomatics.ucalgary.ca/about/whatis
Virrantaus, K. and Haggrén, H. (2000). Curriculum of Geoinformatics -- Integration of Remote Sensing and Geographical Information Technology. International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII, Part B6, pp. 288-294.

Spatial and Geospatial

Often my students ask about the difference(s) between spatial and geospatial. These two words appear very frequently in remote sensing and GIS literature.

The word spatial originated from Latin 'spatium', which means space. Spatial means 'pertaining to space' or 'having to do with space, relating to space and the position, size, shape, etc.' (Oxford Dictionary), which refers to features or phenomena distributed in three-dimensional space (any space, not only the Earth's surface) and, thus, having physical, measurable dimensions. In GIS, 'spatial' is also referred to as 'based on location on map'.

Geographic(al) means 'pertaining to geography (the study of the surface of the earth)' and 'referring to or characteristic of a certain locality, especially in reference to its location in relation to other places' (Macquarie Dictionary). Spatial has broader meaning, encompassing the term geographic. Geographic data can be defined as a class of spatial data in which the frame is the surface and/or near-surface of the Earth. 'Geographic' is the right word for graphic presentation (e.g., maps) of features and phenomena on or near the Earth's surface. Geographic data uses different feature types (raster, points, lines, or polygons) to uniquely identify the location and/or the geographical boundaries of spatial (location based) entities that exist on the earth surface. Geographic data are a significant subset of spatial data, although the terms geographic, spatial, and geospatial are often used interchangeably.

Geospatial is another word, and might have originated in the industry to make the things differentiate from geography. Though this word is becoming popular, it has not been defined in any of the standard dictionary yet. Since 'geo' is from Greek 'gaya' meaning Earth, geospatial thus means earth-space. NASA says 'geospatial means the distribution of something in a geographic sense; it refers to entities that can be located by some co-ordinate system'. Geospatial data is to develop information about features, objects, and classes on Earth's surface and/or near Earth's surface. Geospatial is that type of spatial data which is related to the Earth, but the terms spatial and geospatial are often used interchangeably. United States Geological Survey (USGS) says "the terms spatial and geospatial are equivalent".

Excerpted from my book Remote Sensing and GIS

Remote Sensing and Geographic Information System

We normally observe the earth from a more or less horizontal viewpoint while living on its surface. From an altitude or from a vertical perspective, our impression of the surface below is notably different. Remote sensing enables us to view the spectral and spatial relations of observable objects and materials at a distance, typically from above, using instruments or sensors. Remote sensing is most often practised from platforms such as airplanes and spacecrafts with onboard sensors that survey and analyse surface features over extended areas unencumbered by the immediate proximity of the neighbourhood. It is a practical, orderly, and cost-effective way of maintaining and updating information about the world around us.

The advancements in computer-based image processing have made robotic and manned platform observations accessible to universities, resource-responsible agencies, environmental companies, and even individuals in their personal computers. Initially, remote sensing was controlled and sponsored by the governments of various countries but recently, commercial vendors have also involved themselves in this emerging field.

Geographical Information System (GIS) is a computer-assisted information management system of geographically referenced data. A GIS differs from conventional computer-assisted mapping and attribute data analysis systems. Although computer-assisted cartographic systems emphasize map production and presentation of spatial data, they cannot analyse spatially defined attribute data. Attribute data analysis systems, on the other hand, analyse aspatial data. A GIS blends these into a more powerful analytical tool. Its proponents highlight its capacity to produce a comprehensive and timely analysis of complex database and its potential to improve data collection, analysis, and presentation process. Today, it is possible to make conventional GIS over the Internet, sharing various data for the use of the whole world. From the perspective of information science, the growing interest in GIS is fascinating.

GIS provides an exceptional means for integrating timely remote sensing data with other spatial and thematic data types. It is a concept that originated in Canada four decades ago, is now being applied by several application sectors as the demand increases for information and analysis on the relationship between people and their environment. Now that many commercially available GIS software packages are becoming increasingly user friendly, and can be run on personal computers, this important tool is being actively explored all over the world for various applications.

Remote sensing and GIS were initially recognized as supporting tools for planning, monitoring, and managing the appropriate utilization of earth resources. However, due to their multidisciplinary applications and integration with numerous other scientific and technological fields, in the recent years they have become a distinct field of study.

The rapid progress, and increased visibility, of remote sensing and GIS since the 1990s has been made possible by a paradigm shift in computer technology, computer science, and software engineering, as well as airborne and space observation technologies. As a result a new field of study named geomatics engineering or geospatial technology or geoinformatic technology is now in its maturity. The term 'geomatics' is fairly young and is commonly used to define the tools and techniques used in land surveying, remote sensing, GIS, global navigation satellite systems (GNSS), and related forms of earth mapping.

Beginners in this emerging field may refer my book Remote Sensing and GIS

Global Navigation Satellite Systems

Global Navigation Satellite System (GNSS) is the standard generic term for satellite navigation systems that provide autonomous geospatial positioning information with global coverage. A GNSS allows small electronic receivers to determine their locations (latitude, longitude, and altitude) and precise time information using radio signals transmitted from navigation satellites along a line of sight. The need to determine precise locations for use in a variety of innovative and emerging applications such as surveying, navigation, tracking, mapping, earth observation, mobile-phone technology, and rescue applications is inevitable. Satellite navigation and positioning systems are robust and evolving technology that uses a global network of navigation satellites to achieve this in a variety of ways perhaps as many ways of its applications. GNSS technology is accurate enough to pinpoint locations anywhere in the world, in any weather condition, and at any time of the day.

There are currently several layers of satellite navigation systems. The United States’ GPS is a fully operational GNSS, and Russian GLONASS is partial operational of that kind. Two other such systems are also being developed—European Union’s Galileo and Chinese Compass. All of these four are for or intended towards global coverage. Several regional systems are also available or initiated for regional coverage by several countries. In addition, augmentation systems on these core systems are also offered by several government and private agencies.

The benefits of Satellite Navigation are enormous. For example, the International Civil Aviation Organization and the International Maritime Organization have accepted GNSS as essential in their navigation. GNSS is revolutionizing and revitalizing the way nations operate in space, from guidance systems for the International Space Station’s return vehicle, to the management of tracking and control for satellite constellations. Military applications of GNSS are extremely widespread from mobilizing troop to supply of arms and amenities, aid in rescue operations to missile guidance.

Vehicle manufacturers now provide navigation units that combine vehicle location and road data to avoid traffic jams, and reduce travel time, fuel consumption, and therefore pollution. Road and rail transport operators are now capable to monitor the goods’ movements more efficiently, and combat theft and fraud more effectively by means of GNSS. Taxi companies now use these systems to offer a faster and more reliable service to customers. Delivery service providers are increasingly being dependent on GNSS.

Incorporating the GNSS signal into emergency-services applications creates a valuable tool for the emergency services (fire brigade, police, paramedics, sea and mountain rescue), allowing them to respond more rapidly to those in danger. There is also potential for the signal to be used to guide the blind; monitor Alzheimer’s sufferers with memory loss; and guide explorers, hikers, and sailing enthusiasts.

Surveying systems incorporating GNSS signals are being used as tools for many applications such as urban development. GNSS can be incorporated into geographical information systems for the efficient management of agricultural land and for aiding environmental protection; this is a critical role of paramount importance to assist developing nations in preserving natural resources and expanding their international trade. Another key application is the integration of third-generation mobile phones with Internet-linked applications. It will facilitate the interconnection of telecommunications, electronics, and banking networks & systems via the extreme precision of its atomic clocks.

The role played by the current GNSSs in our everyday lives is set to grow considerably with new demands for more accurate information along with integration into more applications. Some experts regard satellite navigation as an invention that is as significant in its own way as that of the watch: No one nowadays can ignore the time of day, and in the future, no one will be able to do without knowing their precise location.

For further details on GNSS please refer Global Navigation Satellite Systems: Insights into GPS, GLONASS, Galileo, Compass, and Others

Super-resolution reconstruction : A new technique for image enhancement

Spatial resolution enhancement is usually required in both astronomy and earth observing remote sensing fields, especially in satellite images taken with the aim of recognizing objects whose size approaches the limiting spatial resolution scale. One approach to improve the spatial resolution is to use longer focal ratios, which requires larger, better-stabilized and more expensive orbital platforms. Another approach is to use sensor chips with smaller pixel size and increased pixel density. The later is technically difficult, it decreases the amount of collected light by each pixel and increases shot and readout noise. At present, the best cost/benefit ratio seems to be achieved when using digital image processing techniques known as spatial resolution enhancement, super-resolution image reconstruction or simply super-resolution (SR) (Merino and Núñez 2007).

SR refers to the reconstruction methods that can be applied to obtain an image with higher spatial resolution through the use of lower-resolution (LR) image(s). SR techniques are closely related to the problems of image restoration and image interpolation. The purpose of image restoration is to recover a degraded image without changing the pixel density of the image. Based on similar theories as image restoration, SR can be considered as a second generation of image restoration techniques which also change pixel density. Image interpolation techniques can be used to increase the pixel density of an image. Therefore, SR image reconstruction techniques combine image restoration and interpolation to reconstruct one or a set of high-resolution (HR) images from LR image(s).

SR image reconstruction has widely been researched in the last two decades. Most of the researches have been carried out for combining multiple LR images of the same scene to reconstruct a single or more HR image(s) (e.g., Shen et al. 2009; Tsai and Huang 1984; Kim et al. 1990; Kim and Su 1993; Rhee and Kang 1999; Chan et al. 2003). The basic principle underlying most of the aforementioned techniques is to take multiple images from the same object under similar lighting conditions but from slightly different sensor locations or orientations. When two images provide different views of the same object or landscape, the motion or motion vector field (a set of displacements of the pixel grid points between the images) is used to keep the grid points tied to their corresponding fixed locations on the viewed surface. The true motion between the images is not known, and must therefore be approximated that is a complicated and difficult task (Horn 1986; Packalén et al. 2006). Furthermore, most of the aforementioned SR techniques are computationally expensive. Another major limitation of these techniques is perhaps requirement of multiple images that are often costly to procure and difficult to acquire at the same temporal instant, especially for remote sensing. Commonly, the improvement of spatial resolution of multi-frame SR algorithm always has to sacrifice the temporal resolution (Tsai and Huang 1984). Other limitations include non-suitability of LR images for SR reconstruction; the application of SR algorithms is possible only if the images are sub-pixel shifted.

Alternatively, many researchers tackled the image fusion problem of reconstructing an LR image using an HR image. A typical example is the use of panchromatic image for sharpening multi/hyper-spectral images (Wang et al. 2005; Ranchin et al. 2003; Gonzalez-Audicana et al. 2006; Joshi et al. 2006; Park and Kang 2004; Bhatta 2008). However, SR reconstruction and image fusion are different. Image fusion combines one or several LR images with one or more HR images in order to obtain a useful final image with better spatial resolution than LR image. Therefore, fusion methods require the use of at least one HR image and the spatial resolution of their results is limited by that HR pixel-size. In contrast, SR algorithms do not use any HR image; they only depend on LR image(s).

However, most of the researchers of the existing literatures believe that the quality of a single LR image is limited; and interpolation based on an under-sampled image does not allow recovering the lost high-frequency information. Hence single LR image can not be used for SR reconstruction and multiple observations of the same scene are needed. Typical single-frame SR construction techniques have been criticized widely as ‘image enhancement’ by means of image scaling, interpolation, zooming and enlargement (Chan et al. 2008). Despite the criticisms, these approaches are preferred where multi-frame techniques are not applicable or affordable.

Although several techniques for single-frame SR reconstruction have been demonstrated by several researchers, a few of them have addressed remote sensing imageries. Tao et al. (2006) have shown an effective point spread function for single-frame SR reconstruction. Jiji et al. (2004) have proposed a single-frame SR algorithm using a wavelet-based technique where the HR edge primitives are learned from the HR data set locally. Chang et al. (2004) have proposed a single-frame image SR method where the generation of the HR image depends simultaneously on multiple nearest neighbors in the training set in a way similar to the concept of locally linear embedding for manifold learning. This method requires fewer training examples than other learning-based SR methods. The SR method proposed by Begin and Ferrie (2004) is the extension of a Markov-based learning algorithm, capable of processing an LR image with unknown degradation parameters. A different method for enhancing the resolution of LR facial images using an error back projection method based on top-down learning is proposed by Park and Lee (2004). An image hallucination approach based on primal sketch priors is presented by Sun et al. 2003; where reconstruction constraint is also applied to further improve the quality of the hallucinated image. Jiji and Chaudhuri (2006) have demonstrated a single-frame image SR through contourlet learning. This process ensures capturing the HR edges from the training set given a LR observation, as well as captures the smoothness along contours. Other approaches include low level vision learning (Freeman et al. 2000), incorporating the distribution of pixel intensity derivatives (Tappen et al. 2003), pixel classification (Wu et al. 2004; Atkins et al. 1999), locally-adaptive zooming algorithm (Battiato et al. 2002), smart interpolation by anisotropic diffusion (Battiato et al. 2003); triangulation on pixel level (Su and Willis 2004; Yu et al. 2001), Subpixel edge localization (Jensen and Anastassiou 1995), and neural network for interpolation (Staelin 2003). Ouwerkerk (2006) has surveyed several single-frame SR techniques by looking at theoretical backgrounds and practical results.

References
Atkins, C.B., Bouman, C.A. and Allebach, J.P., 1999. Tree-based resolution synthesis. Proceedings of IEEE ICIP (1999), pp. 405–410.
Battiato, S., Gallo, G. and Stanco, F., 2002. A locally-adaptive zooming algorithm for digital images. Image Vision and Computing Journal, 20(11), 805–812.
Battiato, S., Gallo, G. and Stanco, F., 2003. Smart interpolation by anisotropic diffusion. Proceedings of 12th International Conference on Image Analysis and Processing, pp. 572–577.
Begin, I. and Ferrie, F.R., 2004. Blind super-resolution using a learning-based approach. Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR ’04), vol. 2, pp. 85–89, Cambridge, UK.
Bhatta, B., 2008. Remote Sensing and GIS. Oxford University Press, pp. 872.
Chan, J.C.W., Ma, J. and Canters, F., 2008. A comparison of superresolution reconstruction methods for multi-angle CHRIS/Proba images. Proceedings of the SPIE, Vol. 7109, available online at http://dx.doi.org/10.1117/12.800256
Chan, R., Chan, T., Shen, L. and Shen, Z., 2003. Wavelet algorithms for high-resolution image reconstruction. SIAM J. Sci. Comput., 24, 1408–1432.
Freeman, W.T., Pasztor, E.C. and Carmichael, O.T., 2000. Learning low-level vision. International Journal of Computer Vision, 40(1), 25–47.
Gonzalez-Audicana, M., Otazu, X., Fors, O. and Alvarez-Mozos, J., 2006. A low computational-cost method to fuse IKONOS images using the spectral response function of its sensors. IEEE Trans. Geosci. Remote Sens., 44, 1683–1691.
Horn, B., 1986. Robot Vision, MIT Press, Cambridge, MA.
Jensen, K. and Anastassiou, D., 1995. Subpixel edge localization and the interpolation of still images. IEEE Transactions on Image Processing, 4(3), 285–295.
Jiji C.V. and Chaudhuri S., 2006. Single-Frame Image Super-resolution through Contourlet Learning. EURASIP Journal on Applied Signal Processing, DOI 10.1155/ASP/2006/73767
Jiji, C.V., Joshi, M.V. and Chaudhuri, S., 2004. Single-frame image super-resolution using learned wavelet coefficients. International Journal of Imaging Systems and Technology, 14(3), 105–112.
Joshi, M.V., Bruzzone, L. and Chaudhuri, S., 2006. A modelbased approach to multiresolution fusion in remotely sensed images. IEEE Trans. Geosci. Remote Sens., 44, 2549–2562.
Kim, S.P. and Su, W.Y., 1993. Recursive high-resolution reconstruction of blurred multiframe images. IEEE Trans. Image Process., 2, 534–539.
Kim, S.P., Bose, N.K. and Valenzuela, H.M., 1990. Recursive reconstruction of high-resolution image from noisy undersampled multiframes. IEEE Trans. Acoust. Speech Signal Process., 38, 1013–1027.
Merino, M.T. and Núñez, J., 2007. Super-Resolution of remotely sensed images using SRVPLR and SRASW, Proc. of IEEE International Conference on Geoscience and Remote Sensing Symposium, 23-28 July, pp. 4866-4869. Available online at http://ieeexplore.ieee.org/stamp/stamp. ... r=04423951.
Ouwerkerk, J.D., 2006. Image super-resolution survey. Image and Vision Computing, 24, 1039–1052.
Packalén, P., Tokola, T., Saastamoinen, J. and Maltamo, M., 2006. Use of a super-resolution method in interpretation of forests from multiple NOAA/AVHRR images. International Journal of Remote Sensing, 27(24), 5341–5357.
Park, J.H. and Kang, M.G., 2004. Spatially adaptive multi-resolution multispectral image fusion. International Journal of Remote Sensing, 25, 5491–5508.
Park, J.S. and Lee, S.W., 2004. Enhancing low-resolution facial images using error back-projection for human identification at a distance. Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR ’04), vol. 1, pp. 346–349, Cambridge, UK.
Ranchin, T., Aiazzi, B., Alparone, L., Baronti, S. and Wald, L., 2003. Image fusion—the ARSIS concept and some successful implementation schemes. ISPRS J. Photogramm. Remote Sens., 58, 4–18.
Rhee, S. and Kang, M.G., 1999. Discrete cosine transform based regularized high-resolution image reconstruction algorithm. Opt. Eng., 38, 1348–1356.
Shen, H., Ng, M.K., Li, P., and Zhang, L., 2009. Super-Resolution Reconstruction Algorithm To MODIS Remote Sensing Images. The Computer Journal, 52(1), 90–100.
Staelin, C., Greig, D., Fischer, M. and Maurer, R., 2003. Neural network image scaling using spatial errors. HP Laboratories, Israel.
Su, D. and Willis, P., 2004. Image interpolation by pixel level data-dependent triangulation. Computer Graphics Forum, 23(2).
Sun, J., Zheng, N.N., Tao, H. and Shum, H.Y., 2003. Image hallucination with primal sketch priors. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’03), vol. 2, pp. II-729–II-736, Madison, Wis, USA.
Tao, S., Zhigao, Y., Liyu, L., and Qianqing, Q., 2006. Single-Frame Remote Sensing Image Superresolution Reconstruction with Sub-blocks Noniterative Scheme. Proc. of IEEE International Conference on Geoscience and Remote Sensing Symposium, 31 July – 4 August, pp. 1382–1385. Available online at http://ieeexplore.ieee.org/xpls/abs_all ... er=4241504.
Tappen, M.F., Russell, B.C. and Freeman, W.T., 2003. Exploiting the Sparse Derivative Prior for Super-Resolution and Image Demosaicing, 2003.
Tsai, R.Y. and Huang, T.S., 1984. Multi-frame image restoration and registration. Adv. Comput. Vis. Image Process., 1, 317–339.
Wang, Z.J., Ziou, D., Armenakis, C., Li, D. and Li, Q.Q., 2005. A comparative analysis of image fusion methods. IEEE Trans. Geosci. Remote Sens., 43, 1391–1402.
Wu, J., Trivedi, M. and Rao, B., 2004. Resolution enhancement by AdaBoost. Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR ’04), vol. 4, pp. 893–896, Cambridge, UK.
Yu, X., Morse, B.S. and Sederberg, T.W., 2001. Image reconstruction using data dependent triangulation. IEEE Computer Graphics and Application, 21(3), 62–68.

Remote Sensing and GIS for Urban Growth Analysis

Urban growth and sprawl is a pertinent topic for analysis and assessment towards the sustainable development of a city. Environmental impacts of urban growth and extent of urban problems have been growing in complexity and relevance, generating strong imbalances between the city and its hinterland. The need to address this complexity in assessing and monitoring the urban planning and management processes and practices is strongly felt in the recent years.

Determining the rate of urban growth and urban spatial configuration, from remote sensing data, is a prevalent approach in contemporary urban geographic studies. Maps of growth and a classified urban structure derived from remotely sensed data can assist planners to visualise the trajectories of their cities, their underlying systems, functions, and structures. There are currently a number of applications of analytical methods and models available to cities by using the remote sensing data and geographic information system (GIS) techniques, in specific -- for mapping, monitoring, measuring, analysing, and modelling.

The international participants are increasingly engaged with the urgent environmental tasks for the sustainable development of their urban regions, the planning challenges faced by the local authorities, and the application of remote sensing data and GIS techniques in the analysis of urban growth to meet these challenges. However, despite the promise of new and fast-developing remote sensing technologies, a gap exists between the research-focused results offered by the urban remote sensing community and the application of these data and methods/models by the governments of urban regions. There is no end of interesting scientific questions to ask about cities and their growth, but sometimes these questions do not match the operational problems and concerns of a given city. This necessitates more focused research and debate in the areas of urban growth analysis, especially using remote sensing and GIS.

For more detailed discussion please refer Analysis of Urban Growth and Sprawl from Remote Sensing Data